Identifying persons at risk for developing type 2 diabetes in a concentrated population of high risk ethnicities in Canada using a risk assessment questionnaire and point-of-care capillary blood HbA1cmeasurement
Bibliographic record
Abstract
BACKGROUND: Amidst the growing health care burden created by diabetes, this study aimed to assess the utility of a prediabetes/type 2 diabetes risk questionnaire in high risk ethnic communities in Toronto Canada. METHODS: Participants (n = 691) provided questionnaire responses and capillary blood tests collected via fingerstick and results were analysed for HbA1c using the Bio-Rad in2it point-of-care device. The Bland-Altman method was used to compare point-of-care HbA1c analysis (Bio-Rad, boronate affinity chromatography) to that using high performance liquid chromatography. ANOVA and linear regression were performed to investigate the relationship between questionnaire and blood data. RESULTS: Mean (± SD) HbA1c was 5.99% ± 0.84 and the Bland-Altman analysis revealed no significant biases HbA1c (bias = 0.039, 95% limits of agreement = -1.14 to 1.22). ANOVA showed that with increasing risk classification based on questionnaire answers (with the exception of "moderate"-to-"high"), there was a significant increase in mean HbA1c (Welch Statistic 30.449, p < 0.001). Linear regression revealed that the number of high risk parents, age category, BMI, physical activity participation and previous diagnosis of high blood sugar were significant contributors (p < 0.05) to the variance in HbA1c. CONCLUSIONS: Though not a substitute for established diagnostic protocols, the use of a risk questionnaire can be an accurate, low cost, educational and time efficient method for assessment of type 2 diabetes risk. The early detection of prediabetes and type 2 diabetes is vital to increased awareness and opportunity for intervention with the goal of preventing or delaying the progression of type 2 diabetes and the known associated complications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".